The Use of Biomaterials in Islet Transplantation

Pancreatic islet transplantation is a therapeutic option to replace destroyed β cells in autoimmune diabetes. Islets are transplanted into the liver via the portal vein; however, inflammation, the required immunosuppression, and lack of vasculature decrease early islet viability and function. Therefore, the use of accessory therapy and biomaterials to protect islets and improve islet function has definite therapeutic potential. Here we review the application of niche accessory cells and factors, as well as the use of biomaterials as carriers or capsules, for pancreatic islet transplantation

Research on Sentiment Analysis in Software Engineering

Sentiment analysis has various application scenarios in software engineering (SE), such as detecting developers’ emotions in commit messages and identifying developers’ opinions on Q&A forums. Nevertheless, commonly used out-of-box sentiment analysis tools cannot obtain reliable results in SE tasks and misunderstanding of technical knowledge is demonstrated to be the main reason. Then researchers start to customize SE-specific methods in supervised or distantly supervised ways. To assess the performance of these methods, researchers use SE-related annotated datasets to evaluate them in a within-dataset setting, that is, they train and test each method using data from the same dataset. However, the annotated dataset for an SE-specific sentiment analysis task is not always available. Moreover, building a manually annotated dataset is time-consuming and not always feasible. An alternative is to use datasets extracted from the same platform for similar tasks or datasets extracted from other SE platforms. To verify the feasibility of these practices, it is needed to evaluate existing methods in within-platform and cross-platform settings, which refer to training and testing each method using data from the same platform but not the same dataset, and training and testing each classifier using data from different platforms. This study comprehensively evaluates existing SE-customized sentiment analysis methods in within-dataset, within-platform, and cross-platform settings. Finally, the experimental results provide actionable insights for both researchers and practitioners

Beyond relativistic mean-field studies of low-lying states in neutron-deficient krypton isotopes

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The Displacement and Strain Field of Three-Dimensional Rheologic Model of Earthquake Preparation

Based on the three-dimensional elastic inclusion model proposed by Dobrovolskii, we developed a rheological inclusion model to study earthquake preparation processes. By using the Corresponding Principle in the theory of rheologic mechanics, we derived the analytic expressions of viscoelastic displacement U(r, t) , V(r, t) and W(r, t), normal strains epsilon(xx) (r, t), epsilon(yy) (r, t) and epsilon(zz) (r, t) and the bulk strain theta (r, t) at an arbitrary point (x, y, z) in three directions of X axis, Y axis and Z axis produced by a three-dimensional inclusion in the semi-infinite rheologic medium defined by the standard linear rheologic model. Subsequent to the spatial-temporal variation of bulk strain being computed on the ground produced by such a spherical rheologic inclusion, interesting results are obtained, suggesting that the bulk strain produced by a hard inclusion change with time according to three stages (alpha, beta, gamma) with different characteristics, similar to that of geodetic deformation observations, but different with the results of a soft inclusion. These theoretical results can be used to explain the characteristics of spatial-temporal evolution, patterns, quadrant-distribution of earthquake precursors, the changeability, spontaneity and complexity of short-term and imminent-term precursors. It offers a theoretical base to build physical models for earthquake precursors and to predict the earthquakes